DESCRIPTION (provided by candidate): The current research is to study how epigenomic modifier histone deacetylase 3 (HDAC3) regulates carbohydrates metabolism and insulin sensitivity in skeletal muscle in response to either the internal circadian clock or the external dietary factor. I have developed a novel mouse model with HDAC3 specifically depleted in skeletal muscle, and have found that the mice display disrupted metabolic circadian gene expression and exacerbated glucose intolerance that is induced by high fat diet (HFD). During the mentored phase, I will gain new expertise in genome-wide epigenomic approaches that are well established in my mentor's laboratory. I will also gain additional knowledge in muscle physiology, metabolic flux analysis, and metabolomics methods through collaboration with other laboratories and core facilities at University of Pennsylvania. The research that I propose to continue in the independent phase is to study HDAC3 in exercise endurance, fuel selection and efficiency, as well as lipid and amino acid metabolism in skeletal muscle. We have found that mice without muscular HDAC3 have surprisingly improved exercise endurance associated with a switch in fuel preference from carbohydrates towards lipid. I will characterize mitochondrial function and trace metabolic fluxes through lipid, ketone bodies, and amino acids catabolism, including the anaplerotic purine nucleotide cycle, in exercising animals as well as in isolated primary myocytes, where knockdown experiments will test the requirement of specific HDAC3 target genes for the observed fuel selection and enhanced fuel efficiency. My future career goal after independence is to decipher the epigenomic mechanism that underlies hormetic response to physical exercise in skeletal muscle. Exercise is beneficial to many aspects of health, especially in the context of obesity and diabetes. My general hypothesis is that epigenomic mechanisms underlie exercise- induced beneficial metabolic remodeling. I will comprehensively characterize exercise-induced changes in skeletal muscle transcriptome and epigenome using genome-wide methods and metabolomics approaches. This is the first endeavor ever, as far as I know, to analyze exercise-induced epigenomic changes in a genome-wide scale. This unbiased method will produce comprehensive datasets, from which data mining and motif analysis will generate new hypotheses regarding novel transcription networks that respond to exercise. Biochemistry methods and metabolic flux analysis will then be used to validate these hypotheses, followed by development of genetic animal models and physiology studies. Together, these approaches will generate testable hypothesis backed up by preliminary data, which is essential for successful competition for future funding opportunities.